006 Spezielle Computerverfahren
Refine
Year of publication
- 2021 (2)
Document Type
- Article (1)
- Master's Thesis (1)
Language
- English (2)
Has Fulltext
- yes (2)
Keywords
- Adaptive models (1)
- Deep neural networks (1)
- Episodic memory (1)
- Greenhouse (1)
- Memory consolidation (1)
- gestość punktów (1)
- lidar (1)
- point density (1)
- segmentacja drzew (1)
- tree segmentation (1)
Institute
Forests are an important part of the ecosphere and have long been at the center of extensive research. For quantifying forest metrics on larger scales, remote sensing technologies have become important tools over the last decades. One of these tools is airborne lidar, which has enabled detailed analyses of canopy structures by providing three-dimensional point clouds of forests. Within such data sets, individual crown bodies can be detected and delineated, which is referred to as tree crown segmentation. However, among the few available algorithms the most sophisticated ones are also the most resource hungry. Another general issue with lidar point clouds is that their density can vary strongly between adjacent areas which also influences segmentation results. In this study a segmentation workflow from point cloud preparation to the evaluation of results was developed. Part of this workflow was a novel approach to removing undesired density patterns from point clouds, using detailed information on the measurement procedure. Also, a scalable segmentation algorithm, based on 3-dimensional mean shift clustering was implemented. It reached single-core runtimes of about 50 seconds ha -1 on point clouds with a density of 10 points m -2 . The segmentation results allowed for the estimation of stem diameter distributions for a temperate mixed forest stand. Overall, the presented workflow provides a solid basis for individual tree segmentation and can be further extended and scaled with relative ease.
This work presents an adaptive architecture that performs online learning and faces catastrophic forgetting issues by means of an episodic memory system and of prediction-error driven memory consolidation. In line with evidence from brain sciences, memories are retained depending on their congruence with the prior knowledge stored in the system. In this work, congruence is estimated in terms of prediction error resulting from a deep neural model. The proposed AI system is transferred onto an innovative application in the horticulture industry: the learning and transfer of greenhouse models. This work presents models trained on data recorded from research facilities and transferred to a production greenhouse.